This work provides a theoretical justification for the presence of additive compositionality in word vectors learned using the Skip-Gram model.

importantly, this statement requires no assumptions on the distribution P (X, Y ).


In this section, we provide theoretical support for the usefulness of the features learned even when the Skip-Gram model is misspecified.

we state that the best candidate for having the same meaning as the set of context words C is the word We refer to any vector that minimizes (4) as a paraphrase of the set of words C. There are two natural concerns with (4).


UDS emphasizes accessible distinctions, which can be collected through crowd-sourcing.

the events discussed here should not be confused with events as defined in Information Extraction and related tasks such as event coreference (Humphreys et al., 1997), which correspond more closely to the everyday notion of an event, such as a political or financial event, and generally consist of multiple events in the sense discussed here.


SRT schemes diverge in the background and training they require from their annotators.

this approach is less suitable cross-linguistically.


This issue is particularly serious in discussion forum documents, where it is not uncommon to see pronouns serve as subjects and objects of event mentions.

the best-performing KBP 2016 system on English trigger detection achieved only an F-score of 47 .


Third, since our model produces event coreference output in the form of an antecedent vector (with one antecedent per event mention), it needs to be trained on antecedent vectors.

6 For Chinese, we train models on 302 of the training documents, tune parameters on 81 training documents, and report results on the official  Results of event coreference and trigger detection are obtained using version 1.7.2 of the official scorer provided by the KBP 2016 organizers.


• Optimization: We used ADAM update rule (Kingma and Ba, 2014) with an initial learning rate of 0.001, and used negative loglikelihood as the training objective.

in the adaptation step, we used the official dataset OntoNotes Release 5.0 2 which is provided by CoNLL-2012 shared task, to carry out our experiments.


The performance on argument is not so good.

there is work on automatically labeling clause level situation entity types (Palmer et al., 2007;Friedrich et al., 2016).


The final encoder consists of two stacked convolutional networks (Figure 1): CNN-a produces the encoder output z j to compute the attention scores a i , while the conditional input c i to the decoder is computed by summing the outputs of CNN-c, In practice, we found that two different CNNs resulted in better perplexity as well as BLEU compared to using a single one ( §5.3).

this did not cause a change in attention patterns nor did it improve translation accuracy.


Neural Machine Translation (NMT) is an endto-end learning approach to machine transla-tion which has recently shown promising results on multiple language pairs (Luong et al., 2015;Shen et al., 2015;Wu et al., 2016;Tu et al., 2016;Zhang and Zong, 2016;Jean et al., 2015;Meng et al., 2015).

bi-directional RNNs are proposed to integrate information from the past and the future.


Thus, given x we optimize the marginal probability function: where p(y | z) is the Kronecker delta function δ e(z),y , which is 1 if the execution of z, denoted as e(z), generates y and 0 otherwise.

options: Rationale: To solve this easiest way is just put the value and see that if it equals or not.


The advantage of this method is that memory intensive operations, such as attention and the copy mechanism, only need to be unrolled for K steps, and K can be adjusted so that the computation graph fits in memory.

these operations generally involve an exponential number of matrix multiplications as the size of c and z grows.


We represent these semantic neighbours at the character level, and heuristically boost their probability by multiplying the sampling probability of these character strings by their cosine similarity to the key word, plus a constant.

our participants were not financially incentivised, perceiving the evaluation as an intellectual challenge.


Poetry was generated with a variety of themes and poetic devices (see supplementary material).

this forces us to generate poetry according to discrete themes and styles from pretrained models, requiring a new training corpus for each model.


Example 1 shows a data/text unit created by our method using DBPedia as input KB.

for input with a number of triples more than one, the crowd was asked to merge the sentences corresponding to each triple (obtained in step 2) into a natural sounding text.


While in WEBNLG, the same pattern is instantiated in average 2.40 times, it is instantiated 10.31 times in average in RNNLG.

the training corpora built using this method cannot be used to train KB verbalisers i.e., generation systems that can verbalise KB fragments.


Our empirical study on both synthetic and realworld datasets demonstrates the efficiency of COREQA, which is able to generate correct, coherent and natural answers for knowledge inquired questions.

for example, CopyNet (Gu et al., 2016) is able to copy words from the original source in generating the target through incorporating copying mechanism in conventional Seq2Seq learning, but they cannot retrieve SUs from external memory (e.g.


In fact, we found that a lot of questions need more than one fact (about 20% based on sampling inspection).

for example, generating the response "Of course, read" for replying the message "Can you read the word 'read'?"


However, we argue that simply selecting three independent CNNs is mechanical and inflexible.

(2015; 2016) achieve much higher F 1 scores than other methods.


There are several tailor-made languages designed for querying KBs, such as SPARQL (Prudhommeaux and Seaborne, 2008).

2) Bi LSTM+GKI performs better than Bi LSTM, and achieves an improvement of 1.3 points.


We directly use the embedding for each answer aspect through the KB embedding matrix E k ∈ R d×v k .

it could alleviates the out-of-vocabulary (OOV) problem, which helps the crossattention model to represent the question more precisely.


Here, v k means the vocabulary size of the KB resources.

we argue that simply selecting three independent CNNs is mechanical and inflexible.


While in general neither humans nor DCP agents compute explicit representations of this posterior, past work has found that both humans and suitably-trained neural networks can be modeled as Bayesian reasoners (Frank et al., 2009;Paige and Wood, 2016).

the translation criterion in the previous section makes no reference to listener actions at all.


As an example, Figure 1 depicts a driving game in which two cars, which are unable to see each other, must both cross an intersection without colliding.

dCP agents instead communicate with an automatically induced protocol of unstructured, real-valued recurrent state vectors-an artificial language we might call "neuralese," which superficially bears little resemblance to natural language, and thus frustrates attempts at direct interpretation.


As distributional word vectors, we use the word2vec representations provided by  (trained with CBOW, 5-word context window, 10 negative samples, 400 dimensions).

whereas Zarrieß and Schlangen (2016) propose a strategy to avoid object names when the systems confidence is low, we focus on improving the generation of object names, using distributional knowledge as an additional source.


the system generates tree vs. man) are particularly disturbing for users.

here, the idea is to exploit a powerful model of lexical similarity induced from large amounts text for being able to capture inherent lexical relations between object categories.


From each pair, we randomly include the singular or plural noun in the set of zero-shot nouns.

another approach is to keep visual and distributional information separate, by training a separate visual classifier for each word w in the vocabulary.


For instance, the caption two elephants standing next to each other in a grass field is associated with the decoy a herd of giraffes standing next to each other in a dirt field (errors: herd, giraffe, dirt) or with animals are gathering next to each other in a dirt field (error: dirt; infelicities: animals and gathering, which are both pragmatically odd).

the IC model tends to be biased toward the foil captions, on which it achieves an accuracy of 45.44%, higher than the VQA models.


Experiments on datasets from the CoNLL-X and CoNLL-XI shared tasks show that a non-monotonic dynamic oracle outperforms the monotonic version in the majority of languages.

1: function LOSS(c = λ1|i, λ2, j|B, A , tG) 2: if j > right ∨ 7: (j = right ∧ i < left) then 8: U ← u ∪ {x → y} 9: I ← tG \ U Variable I is for I(c, tG) 10: depending on the upper bound implemented, and will return 0 in case we use the lower bound.


To predict sequences on unannotated text when trained on crowd labels, we consider two broad approaches: (1) directly train the model on all individual crowd annotations; and (2) induce consensus labels via Task 1 and train on them.

due to the technical difficulty of the joint approach with CRFs, they resorted to strong modeling assumptions.


Needless to say, the ability to obtain this annotated data for many languages is limited.

like the standard VAE, we assume the prior of the latent variable z is a diagonal Gaussian distribution with zero mean and unit variance.


timization methods such as stochastic gradient descent (SGD) (Robbins and Monro, 1951); stochastic methods of this type are particularly important for training with large data sets.

recently, recurrent neural networks (rNNs) have shown promising performance on this task (Mikolov et al., 2010;Sutskever et al., 2011).


In RNNs the corresponding decoding function is p(y|h), described in Section 3.3.

for a length-T sequence, denote y t = x t+1 for t = 1, .


The dropout has been applied in each hidden layer and in the final classification layer.

notice that other low-dimensional approximations of kernel functions have been studied, as for example the randomized feature mappings proposed in Rahimi and Recht (2008).


Notice that other low-dimensional approximations of kernel functions have been studied, as for example the randomized feature mappings proposed in Rahimi and Recht (2008).

improving the efficiency of kernel-based methods is a largely studied topic.


Length of the straight lines represents saccade distance and size of the circles represents fixation duration load results in longer fixation duration (Kliegl et al., 2004).

the improvement by adding the gaze component to the CNN network is just 1.34%, which is statistically insignificant over CNN with text component.


We hope to learn vector representations of the most representative aspects for a review dataset.

all these benefits make it a promising alternative to LDa-based methods in practice.


Then, we replace the topic keyword into a variable A to extract patterns, e.g., "I support A."

using this approach, we collected 25,805,909 tuples corresponding to 3,302,613 users and 4,899 topics.


Some arguments play indispensable roles in an event, and serve as vital clues when distinguishing different events.

it demonstrates the effectiveness of our TR methods.


(Ahn, 2006;Ji and Grishman, 2008;Hong et al., 2011;McClosky et al., 2011;Li et al., 2013Li et al., , 2014Nguyen and Grishman, 2015;Nguyen et al., 2016).

• The experimental results show that the quality of our large scale automatically labeled data is competitive with elaborately humanannotated data.


(1) we delete the human annotated ACE data for these mapped event types in ACE dataset and add our automatically labeled data to remainder ACE training data.

we define the objective function on the bags.


In the future we will try our analytical method on other parts of language.


It first identifies individual words, then expands them to chunks, and finally to time expressions.

we define three main syntactic token types, namely time token, modifier, and numeral, to group time-related token regular expressions.


As the training progresses, the prediction branch gradually learns the basic prediction ability.

3), which can be seen as a denoising step within the bag.


Before proceeding to training details, we first discuss how we characterize the noise level of the data by controlling the trace of its transition matrix.

since the global transition matrix only captures the global noise pattern, it fails to characterize individuals with subtle differences, resulting in a performance drop compared to the dynamic one ( TM).


As discussed in Section 2.1, most previous methods to learn embedding mappings use variants of gradient descent.

our experiments on bilingual lexicon induction and crosslingual word similarity show that our method is able to learn high quality bilingual embeddings from as little bilingual evidence as a 25 word dictionary or an automatically generated list of numerals, obtaining results that are competitive with state-of-the-art systems using much richer bilingual resources like larger dictionaries or parallel corpora.


This way, following the notation in Section 3.1, we set While we find that independently computing the similarity measure between all word pairs is prohibitively slow, the computation of the entire similarity matrix XW Z T can be easily vectorized using popular linear algebra libraries, obtaining big performance gains.

for that reason, we carried out some experiments in crosslingual word similarity as a way to test our method in a different task and allowing to compare it to systems that use richer bilingual data.


Most methods to learn these multilingual word embeddings make use of large parallel corpora (Gouws et al., 2015;Luong et al., 2015), but there have been several proposals to relax this requirement, given its scarcity in most language pairs.

a good solution would map source language words close to their translation equivalents in the target language space, and they would thus have their corresponding embeddings nearby, making the optimization value large.


Groundhog Day is a Bill Murray movie which came out in 1993.

we would also like to acknowledge the volunteers from Carnegie Mellon University and Microsoft Research for helping us with the human evaluation.


The scalar probabilities q t j are passed as is to the dialogue policy, and the final summary vector iss ].

the large size of the state vector would lead to a large number of parameters in the policy network.


More interestingly, we see that semantic information is also reflected in these distances.

we divide the image into a grid, and extract all of the image crops whose boundaries sit on the grid lines.


p(s t |s t−1 , W ) is represented by an HMM state transition given W , and the conversion from W to HMM states is deterministically performed by using a pronunciation dictionary through a phoneme representation.

current systems lean heavily on the scaffolding of complicated legacy architectures that grew up around traditional techniques.


The major trends relate to loan translations (Jahr, 1999), or the impact of canonical texts, such as Luther's translation of the Bible to German (Russ, 1994) or the case of the King James translation to English (Crystal, 2010).

we hypothesize that indirect translation will obscure the markers of the original language in the final translation.


The corresponding confusion matrix, presented in Figure 2 (left), reveals interesting phenomena: much of the confusion resides within language families, framed by the bold line in the figure.

cognitively speaking, all translations, regardless of the source and target language, are susceptible to the same constraints.


The original signal of the source language is distorted twice: first via a Germanic language (English) and then via a Romance language (French).

for classification, we used Platt's sequential minimal optimization algorithm (Keerthi et al., 2001;Hall et al., 2009) to train support vector machine classifiers with the default linear kernel.


Interference, by definition, is a language-pair specific phenomenon; isomorphic structures shared by the source and target languages can easily replace one another, thereby manifesting the underlying process of cross-linguistic influence of the source language on the translation outcome.

translations from related sources tend to resemble each other to a greater extent than translations from more distant languages.


Figure 2a presents the mean TF times for determiners in all five native languages, suggesting that native speakers of Portuguese and Spanish, which do have determiners, do not exhibit reduced reading times on this structure compared to natives.

the more similar are the languages, the more similar are the reading patterns of their native speakers in English.


Moreover, morphosyntactic regularities have been shown over these word representations, whereby pairs of words sharing the same relationship exhibit equivalent difference vectors (Mikolov et al., 2013b).

such a collection will highlight a system's capability of distinguishing frequent letter sequences from the semantic contribution of this letter sequence in a word.


In this supervised method, the morpheme segmentation task is modeled as a sequence-to-sequence learning problem, whereby the sequence of labels defines the boundaries of morphemes (Ruokolainen et al., 2013(Ruokolainen et al., , 2014.

in this stage we perform morpheme segmentation using the knowledge gained from the first stage.


For each language considered, the respective Wikipedia dump was preprocessed using an available code 3 .

when checking for the morphological relation between two words, we not only check for the semantic relatedness of the pair at hand (local), but also check if the difference vectors of pairs showing similar orthographic change are consistent (vocabulary-wide).


Data preprocessing was done as in (Johnson and Zhang, 2016).

we call it deep pyramid CNN (DPCNN), as the computation time per layer decreases exponentially in a 'pyramid shape'.


A vocabulary of 30K words is only a small portion of it, but it covers about 98% of the text and produced good accuracy as reported below.

with DPCNNs, the total computation time is bounded by a constant -twice the computation time of a single block, which makes our deep pyramid networks computationally attractive.


(2) Finding the core relation (chains) for each topic entity 2 selection from a much smaller candidate entity set after re-ranking.

first, in order to deal with the unseen relations, we propose to break the relation names into word sequences for question-relation matching.


Relation Detection in KBQA Systems Relation detection for KBQA also starts with featurerich approaches Bast and Haussmann, 2015) towards usages of deep networks (Yih et al., 2015;Dai et al., 2016) and attention models (Yin et al., 2016;Golub and He, 2016).

all word vectors are initialized with 300-d pretrained word embeddings (Mikolov et al., 2013).


We provide our model with both types of relation representation: word-level and relationlevel.

for future work, we will investigate the integration of our HR-BiLSTM into end-to-end systems.


(containing word "play") in the embedding space.

in order to assess how the proposed improved relation detection could benefit the KBQA end task, we also propose a simple KBQA implementation composed of two-step relation detection.


We use WebQSP for the analysis purposes.

their relation embeddings are still trained in supervised way and the number of relations is not large in the experiments.


Four unsupervised algorithms (Tf-Idf, Tex-tRank (Mihalcea and Tarau, 2004), SingleRank (Wan and Xiao, 2008), and ExpandRank (Wan and Xiao, 2008)) and two supervised algorithms (KEA (Witten et al., 1999) and Maui (Medelyan et al., 2009a)) are adopted as baselines.

we would like to thank Jiatao Gu and Miltiadis Allamanis for sharing the source code and giving helpful advice.


We propose a generative model for keyphrase prediction with an encoder-decoder framework, which can effectively overcome the above drawbacks.

z is the sum of all the scores and is used for normalization.


Since the NUS dataset did not specifically mention the ways of splitting training and testing data, the results of the supervised baseline models are obtained through a five-fold cross-validation.

we collected a large amount of high-quality scientific metadata in the computer science domain from various online digital libraries, including ACM Digital Library, Sci-enceDirect, Wiley, and Web of Science etc.


Keyphrase provides highly-summative information that can be effectively used for understanding, organizing and retrieving text content.

these features only target to detect the importance of each word in the document based on the statistics of word occurrence and co-occurrence, and are unable to reveal the full semantics that underlie the document content.


All language model features are trained on the training proportion of each dataset, with 8-gram wordbased setting and Kneser-Ney smoothing (Kneser and Ney, 1995) trained by SRILM toolkit (Stolcke, 2002).

apart from releasing the dataset, they also proposed a methodology that anonymizes the named entity tokens in the data, and these tokens are also re-shuffle in each sample.


• Refill Candidate into Query As a characteristic of the cloze-style problem, each candidate can be refilled into the blank of the query to form a complete sentence.

most of the previous works are only relying on query-to-document attention, that is, only calculate one document-level attention when considering the whole query.


While there is evidence that one person using a first-person singular pronoun increases the likelihood that her conversation partner will as well , we may also expect that one person using first-person singular pronouns may cause the other to use more second-person pronouns, so that both people are referring to the same person.

words are difficult to divorce from their meanings, and sometimes repeating a word conflicts with repeating its referent.


Since PATHDEV is a measure of failure, but collaborative task performance is a measure of success, the negative correlation between PSO and collabora-tive task performance is consistent.

the information density in language, i.e., the distribution of entropy (predictability), reveal the discourse structure to some extent.


We also conduct Ljung-Box test (Ljung and Box, 1978) to examine how the entropy series is different from white noise.

the task for them is to reproduce the giver's route on the follower's map.


For our experiments, we have chosen the following affect categories -positive emotion, anger, sad, anxiety, and negative emotion (which is a superclass of anger, sad and anxiety).

2001, LIWC is based on a dictionary, where each word is assigned to a predefined LIWC category.


While such a global model is effective, it requires re-estimating a model from scratch on all K + 1 domains each time a new domain is added.

we assume K domainspecific intent and slot models trained on respective domains.


The similarities of test domains, represented by overlapping percentage, with experts or source domains are represented in Table 2.

when trained  Table 4: Intent classification accuracy (%) and slot tagging F1-score (%) of our baselines and variants of DE.


Our work is related to both recent advancement in encoder-decoder dialog models and generative models based on CVAE.

different strategies to recover from non-understanding .


Further, we notice that the generated text exhibits similar dialog acts compared to the ones predicted separately by the model, implying the consistency of natural language generation based on y.

we found that the learned latent space is highly correlated with the dialog act and length of responses, which confirms our assumption.


The resulting model achieves better performance on word ordering, parsing and machine translation.

we assume that even with the presence of linguistic feature y regarding x, the prediction of x bow still only depends on the z and c. we have: Collection of Multiple Reference Responses we collected multiple reference responses for each dialog context in the test set by information retrieval techniques combining with traditional a machine learning method.


In this paper, we have presented an algorithm for generating contrastive feedback for a hearer who has misunderstood a referring expression.

we might obtain a more succinct, and possibly better, RE by dropping such redundant information from the RE.


In the rest of this section, we use English sentences as examples to explain our methods.

we force the decoder to output reference translations, as to get automatic alignments between input sentences and their reference translations.


Phrase level, or even sentence level linguistic features are harder to obtain for a generation task such as machine translation, since this would require incremental parsing of the hypotheses at test time.

the degrees of coupling the word and structural label sequences in these three encoders are like this: Mixed RNN encoder > Hierarchical RNN encoder > Parallel RNN encoder.


For Y 's parse tree T , instead of directly modeling the tree itself, we predict a parsing action sequence A which can map Y to T .

as there is no golden annotation of parse trees over the target training data, we use pseudo parsing results as the target dependency references, which are got from an in-house developed arc-eager dependency parser based on work in (Zhang and Nivre, 2011 Table 1: Evaluation results on Chinese-English translation task with BLEU% metric.


In addition to translation quality, we compare the perplexity (PPL) changes on the development set in terms of numbers of training mini-batches for RNNsearch and SD-NMT in Figure 4.

our model is more likely to generate the correct verb "is" with singular form.


Intuitively, propagation paths provide further clue for determining the truthfulness of information since they embed the route and context of how the propagation happens.

first, we extracted the popular source tweets 2 that are highly retweeted or replied.


(De Choudhury et al., 2012) identify more than 200 moods frequent on Twitter as extracted from psychological literature and filtered by AMT workers.

we gain no improvements based on either of these methods, thus reflecting the importance of using high-quality training data and the utility of our strict pipeline.


This result is expected based on the results from Figure 3, which showed how political term usage varies across groups, and how it is especially dependent on political engagement.

data pooling lead to worse results than EasyAdapt.


These results suggest that to predict aisle-crossing Republicans it would be useful to check for usage of typically Democrat-associated frames, especially if those frames are infrequently used by Republicans.

other works focus on identifying and measuring political ideologies (Iyyer et al., 2014;Bamman and Smith, 2015;Sim et al., 2013), policies (Nguyen et al., 2015, and voting patterns (Gerrish and Blei, 2012).


We use the development and validation sets for model selection.

while the model can be naturally generalized to integrate characterlevel attention for known words, the original hybrid model proposed by Luong and Manning (2016) does not use any character-level information for known words.


One of the most successful approaches to grammatical error correction (GEC) is to cast the problem as (monolingual) machine translation (MT), where we translate from possibly ungrammatical English sentences to corrected ones (Brockett et al., 2006;Gao et al., 2010;Junczys-Dowmunt and Grundkiewicz, 2016).

the next hidden stated s is then defined as: where y s−1 is the embedding of the output token at time s-1.


Here λ is a hyper-parameter that balances the weights of the neural network model and the language model.

the state d c n for step n in the characterlevel decoder is defined as follows, where GRU c dec are parameters for the gated recurrent cell of this decoder: if the corresponding token in the source x zs is also an out-of-vocabulary word, we define a second nested level of character attention and use it in the character-level decoder.


(Sankoff and Kruskal, 1983), and Longest Common Subsequence (LCS) (Friedman and Sideli, 1992).

for canonical Xf c we use by default the best aggregation for the task as observed in table 3.


Anecdotally, Grudin (2009) observes a "head-tohead" relation between artificial intelligence and human-computer interaction in research funding.

in scientific research, for example, it could simply be the progress of science, i.e., newer ideas overtake older ones deemed less valuable at a given time; on the other hand, history suggests that it is not always the correct ideas that are most expressed, and many other factors may be important.


These errors are fixed directly in the corpus because they should not impact the annotations of sentence pairs.

the fundamental principles of compositional distributional semantics, henceforth referred to as CDS, are mainly propagated with papers written on the topic.


The presented procedure of building a dataset was tested on Polish.

'Two children are sitting on camels near high mountains.')


entailment+neutral covers neutral+entailment as well, representing the same type of relation between two sentences).

a cursory check shows that these images are quite homogeneous, with a predominant number of dogs depictions.


The SICK corpus  consists of 10K pairs of English sentences containing multiple lexical, syntactic, and semantic phenomena.

the procedure of building the SICK corpus cannot be adapted without modifications.


To achieve robust improvement, systems should be measured according to a variety of metrics beyond simple accuracy.

this was because they presented a comparison of a wide range of linguistic features focusing on second-language acquisition and their method can be applied to plain text.


MS is supported by an NSF Graduate Research Fellowship.

this process of labeling and splitting continues until every branch of recursion bottoms out in singleton spans, at which point the full parse tree can be returned.


All parameters (including word and tag embeddings) are randomly initialized using Glorot initialization (Glorot and Bengio, 2010), and are tuned on development set performance.

at the core of both of these components is the issue of span representation.


In particular, our representation of spans and the form of our label scoring function were directly inspired by their work, as were our handling of unary chains and our use of an empty label.

requiring one label evaluation and at most n − 1 split point evaluations, the running time of the procedure is O(n 2 ).


Our goal is to preserve the basic algorithmic properties of span-oriented (rather than transition-oriented) parse representations, while exploring the extent to which neural representational machinery can replace the additional structure required by existing chart parsers.

we select the label that satisfies omitting span scores from consideration since singleton spans cannot be split.


We implement a practical parser in the new framework with a statistical disambiguation model.

zhang and McDonald (2012) proposed a generalized higher-order model that abandons exact search in graph-based parsing in favor of freedom in feature scope.


V represents the vocabulary size of the target language.

these models may generate incorrect words due to even a single bit error.


The constraints described here are very general requirements for bit arrays, which still allows us to choose between a wide variety of mapping functions.

the binary code prediction model also introduces problems of robustness due to these strong constraints.


Our analysis indicates that this kind of approach should take into account factors such as the encoding layer and the type of word representation.

this research was carried out in collaboration between the HBKU Qatar Computing Research Institute (QCRI) and the MIt Computer Science and Artificial Intelligence Laboratory (CSAIL).


Neural machine translation (MT) models obtain state-of-the-art performance while maintaining a simple, end-to-end architecture.

the inherent difficulty in translating Arabic to Hebrew/German may affect the ability to learn good representations of word structure.


In this way the experimental setup is  Figure 8 shows POS and morphological tagging accuracy of word-based representations from the NMT encoders, as well as corresponding BLEU scores.

word-based models are sufficient for learning the structure of common words.


The output of the LSTM cell h i,t is then fed into a dense layer and finally into a softmax layer (line 38 to 39).

for the fusion, the hierarchical fusion framework was used.


In our experiments, we obtained best results with 32 feature maps (f m ) with the filter-size of 5 × 5 × 5 (or f d × f h × f w ).

for extracting features from the textual modality, we use a CNN (Karpathy et al., 2014).


LSTM (Hochreiter and Schmidhuber, 1997) is a kind of RNN, an extension of conventional feedforward neural network.

out of 79 videos in the dataset, 59 videos are considered in the train/val set.


before addressing interactivity, we apply tandem anchors to real world data, but with anchors gleaned from metadata.


This modification is similar in spirit to supervised anchor words (Nguyen et al., 2015).

not only is our approach more scalable than the Interactive Topic Model, but we also achieve Figure 3: Classification accuracy and coherence using topic features gleaned from user provided multiword and single word anchors.


In addition to facilitating discovery of topical trends (Gardner et al., 2010), topic modeling is used for a wide variety of problems including document classification (Rubin et al., 2012), information retrieval (Wei and Croft, 2006), author identification (Rosen-Zvi et al., 2004), and sentiment analysis (Titov and McDonald, 2008).

regularization allows us to add something which is mathematically similar to priors, and has been shown to improve the vanilla anchor word algorithm (Nguyen et al., 2014).


7 The user can remove an anchor word or drag words from 7 While we use topics generated using harmonic mean for our final analysis, users were shown topics generated using the min combiner.

a form of the Gram-Schmidt process on Q finds anchor words {g 1 .


A form of the Gram-Schmidt process on Q finds anchor words {g 1 .

for the algorithm to be interactive we must also consider runtime.


Since we propose a jointmodel, we cannot directly apply their pretraining trick on entities separately.

we use the same data splits as Li and Ji (2014) and Miwa and Bansal (2016) such that there are 351 documents for training, 80 for development and the remaining 80 documents for the test set.


In Section 7, we perform error analysis to understand the difference in the performance of the two models in detail.

we also plan to extend the identification of entities to full entity mention span instead of only the head phrase as in Lu and Roth (2015).


Thus, for our approach described so far if we only compute the argmax on our objective then we limit our model to output only one relation label per token.

our model comprises of a multi-layer bidirectional recurrent network which learns a representation for each token in the sequence.


For each token in the sequence, we perform a softmax over all candidate tags to output the most likely tag: Our network structure as shown in Figure 2 also contains connections from the output y t−1 of the previous time step to the current top hidden layer.

we train our network by maximizing the logprobability of the correct entity E and relation R tag sequences jointly given the sentence S as below: we can decompose our objective into the sum of log-probabilities over entity sequence and relation sequence.


We use the gold entity tags while training.


Table 4 clearly shows that the overall performance of all methods is lower for verb similarity.

one line of active research is post processing the word vector embedding by transforming the space using a single or multi-relational objective (Yih et al., 2012;Tang et al., 2014;Chang et al., 2013;Tang et al., 2014;Zhang et al., 2014;Faruqui et al., 2015;Mrkšić et al., 2016).


The MSW thesaurus has much lower coverage, but 100 % accuracy, which is why when  combined with the signed clustering the performance is 0.95.

the improvement using both signed clustering as well as thesaurus look is also larger.


A word is represented by a row vector and a column vector depending on its position in the table.

with a dense attention vector, it is computationally expensive to perform the convex combination of m matrices in each iteration.


Generally, ITransF with sparse attention has slightly better or comparable performance comparing to dense attention.

knowledge bases suffer from incompleteness despite their formidable sizes (Socher et al., 2013;West et al., 2014), leading to a number of studies on automatic knowledge base completion (KBC) (Nickel et al., 2015) or link prediction.


Nevertheless, templates are important in a live system to motivate users to interact with it in early stages.

all of these systems make strong assumptions on the structure of queries: they use manually engineered rules that can only generate a subset of SQL, require lexical matches between question tokens and table/column names, or require questions to have a certain syntactic structure.


As we mentioned in the introduction, acquiring labeled training data for discourse relations is a timeconsuming process since it would require human annotators to inspect the full discussions.

our model does not group them into the same cluster and later makes different predictions.


The max in Equation 2 is not the same as the one used for prediction, in Equation 1.

while we focus on monological argumentation, our model could be extended to dialogs, for which argumentation theory thoroughly motivates non-tree structures (Afantenos and Asher, 2014).


Xiao and Cho (2016) extended that architecture by inserting a recurrent neural network layer between the convolutional layer and the classification layer.

indeed, on the MFC and Movies tasks, the discourse-ignorant ADDiTiVE outperforms the FULL model.


Our model trained within the proposed framework provides significant improvement, showing the benefits of utilizing implicit connectives at training time.

we achieve objective (b) by minimizing the discriminator's chance of correctly telling apart the features: The parameters of the augmented network a-CNN, denoted as θ A , can be learned by simply fitting to the data, i.e., minimizing the cross-entropy loss as follows: As mentioned above, here we use the same classifier C as for the implicit network, forcing a unified feature space of both networks.


(2010) developed a two-step approach by first predicting implicit connectives whose sense is then disambiguated to obtain the relation.

as a straightforward way of incorporating connectives, we see that the method slightly improves over the stand-alone CNN, while falling behind our approach with a large margin.


The basic idea of the proposed approach is simple.

we build a discriminator which distinguishes between the features by the two counterpart networks.


Additionally, for each edge mistake more, both loss functions increase, implying monotonicity.

in addition to the design of an algorithm enabling the use of our ∆ ρ , there are other intricacies  caused by the lack of factorization that need to be taken into account (see the next section).


Likewise for B 3 , the feature values can be derived using (ii) and (iii).

we analyzed the theory behind this pro-cess by also contributing to the definition of the properties of loss optimality.


For learning MELA, we generated training and test examples from LSP F according to the procedure described in Section 4.3.

∆ F and ∆ Y J may output identical values for different clusterings that can have a big gap in terms of MELA.


In order to have more confidence in our results, we perform bootstrap resampling experiments in which the training set, evaluation set, and lexical resources are randomized across several replications of the same experiment (for each of the data conditions).

the draw P LOC ∼ PYP(d, α, H LOC ) is an "adapted" version of H LOC .


It is interesting to note that our model performs worse relative to the baseline for English; one possible explanation is that the baseline uses hand-engineered features whereas ours does not, and these features may have been tuned using English data for validation.

it avoids possible problems that might arise when leveraging noisy out-of-domain knowledge bases, which we may explore in future.


The generative approach significantly outperforms the discriminative baseline at any lexicon size, although its advantage drops for smaller lexicons or larger training corpora.

the probability of producing $ controls the typical string length under H y .


This says that y 1:2 has jointly emitted x 1:2 , an event with prior probability P LOC (New York).

we use the same set of languages as Li et al.


We first define the objective function of the GDANs, and then present an algorithm to optimize the given objective function.

we will drop the superscript • (i) when the context is unambiguous.


While achieving stateof-the-art performance, these models rely on a large amount of labeled data.

in this section, we will describe the model architecture and the training algorithms for the GDANs.


We approximate the expectation E P G (q|p,a) by sampling one instance at a time from P G (q|p, a) and then do an update step.

there is discrepancy between the model-generated data distribution and the human-generated data distribution, which leads to suboptimal discriminative models.


Appendix B lists the full set of program tokens, Z, and how they are executed.

the log marginal likelihood of the data is then where to estimate our model parameters θ, we maximize J MML with respect to θ.


Gas tanks tend to be easily punctured, thin-walled containers, while the latest hydrogen tanks are made from Kevlar.

also [e(y t−1 ), d t−1 ] means a concatenation of the vectors e(y t−1 ), d t−1 .


In general, abstractive summarization, aims to cover all the salient points of a document in a compact and coherent fashion.

note that Equation 12ensures that state of the LSTM at time step t is orthogonal to the previous history.


we compare the performance of the following models: • Vanilla e-a-d: This is the vanilla encoderattention-decoder model adapted to the problem of abstractive summarization.


Figure 7: Examples of abstractive summaries produced by our model (bold denotes novel words).

we hypothesize that an early intervention method such as coverage is preferable to a post hoc method such as temporal attention -it is better to inform the attention mechanism to help it make better decisions, than to override its decisions altogether.


Peyrard and Eckle-Kohler (2016a) used metaheuristics to minimize JS divergence in a multi-document summarization approach and showed that the resulting extractive summaries also scored competitively using ROUGE.

munkres-Kuhn only yields the valid matching for one particular set of sentences.


Especially, there is no prior work in optimization-based extractive summarization which has developed an approximation of AP and used it in an objective function.

7 LexRank (Erkan and Radev, 2004) is a popular graph-based approach.


We also thank Alexander M. Rush for providing the dataset for comparison and helpful discussions.

in abstractive sentence summarization, there is no explicit alignment relationship between the input sentence and the summary ex-cept for the extracted common words.


PARADISE in particular is perhaps the first work on learning an automatic evaluation function for dialogue, accomplished through linear regression.

the last row (25% at random) corresponds to the ADEM model trained on all model responses, but with the same amount of training data as the model above (i.e.


UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units.

using better input encoding allowing virtual look-ahead and look-behind in the token representation, TUPA BiLSTM obtains substantially higher scores than TUPA MLP and all other parsers, on both primary and remote edges, both in the in-domain and out-of-domain settings.


End-to-end neural machine translation (NMT), which leverages neural networks to directly map between natural languages, has gained increasing popularity recently Bahdanau et al., 2015).

we find that while the attention identifies the 10-th source word "kuadaxiyang" (cross-atlantic) to be most relevant, the relevance vector of the target word R y 9 finds that multiple source and target words should contribute to the generation of the next target word.


We find that while the attention identifies the 10-th source word "kuadaxiyang" (cross-atlantic) to be most relevant, the relevance vector of the target word R y 9 finds that multiple source and target words should contribute to the generation of the next target word.

for element-wise multiplication such as We use the following method to calculate its weight ratio: for maximization such as v = max{u 1 , u 2 }, we calculate its weight ratio as follows: the general local redistribution rule for LRP is given by Algorithm 1 gives the layer-wise relevance propagation algorithm for neural machine translation.


Our system architecture is generic and can be applied to any classification task, and we expect it to be of use in many annotation projects, especially when dealing with non-standard data or in out-of-domain settings.

to this, we focus on detecting errors in automatically labelled data.


In graph-based extractive summarization, a graph G is constructed to rank the original sentences.

previous research is mostly restricted in one or a few of the subproblems or specific domains (Woodsend and Lapata, 2012;Thadani and McKeown, 2013;Cheung and penn, 2014;pighin et al., 2014;Sun et al., 2015).


Information incorrectness occurs because, for a decoder, it aims at generating a fluent sentence according to the input representation.

she had £ ##,### savings in the bank which meant she was not entitled .


Our choice of a probabilistic model provides a natural test: how surprised is our model by held-out languages?

this goal would require a more complex approach.


Perhaps one of the simplest typological questions comes from phonology: which vowels tend to occur and co-occur within the phoneme inventories of different languages?

one might do better to represent a vowel's pronunciation not by a formant vector, but by a conditional probability distribution over its formant trajectories given its context, or by a parameter vector that characterizes such a conditional distribution.


Although the shared-private model separates the feature space into shared and private spaces, there is no guarantee that sharable features do not exist in private feature space, or vice versa.

moreover, we fully exploit eight corpora with heterogeneous segmentation criteria to model the underlying shared information.


(3) In the third block, we introduce adversarial training.

the main reason might be that the proposed three sharing models implicitly attempt to keep invariant features by shared parameters and learn discrepancies by the task layer.


We simply map traditional Chinese characters to simplified Chinese, and optimize on the same character embedding matrix across datasets, which is pre-trained on Chinese Wikipedia corpus, using word2vec toolkit (Mikolov et al., 2013).

each character in a sentence is labeled as one of L = {B, M, E, S}, indicating the begin, middle, end of a word, or a word with single character.


We present a feed-forward neural network model in Figure 2.

this is larger than Chen and Manning (2014) (200 dimensions) or  (1,024 or 2,048 dimensions).


One is the pipelined method and the other is the joint learning method.

it means that our method can effectively associate two entities when compared LSTM-CRF and LSTM-LSTM which pay little attention to the relational tags.


In this section, we firstly introduce how to change the extraction problem to a tagging problem based on our tagging method.

it is widely used in machine translation (Kalchbrenner and Blunsom, 2013;Sutskever et al., 2014) and sequence tagging tasks (Lample et al., 2016;Vaswani et al., 2016).


It contains a bi-directional Long Short Term Memory (Bi-LSTM) layer to encode the input sentence and a LSTM-based decoding layer with biased loss.

we conduct experiments on a public dataset 1 which is produced by distant supervision method (Ren et al., 2017) to validate our approach.


table 6: Our entity discovery official performance (English only) in KBP2016 is shown as a comparison of three models trained by different combinations of training data sets.


Most of the time (typically 85% for the two datasets), PreWin is sufficient for an accurate classification.

future work may involve testing PreWin on an NER task to see if and how it can generalise to a different classification task and how the results compare to the SOTA and similar methods such as that of Collobert et al.


Hence, they would benefit from incorporating such complementary directed knowledge, both visual and textual.

since this is a recent dataset, we list previous works' results as reported by the MsR-VTT dataset paper itself.


For our video captioning as well as entailment generation results, we use four diverse automatic evaluation metrics that are popular for image/video captioning and language generation in general: METEOR (Denkowski and Lavie, 2014), BLEU-4 (Papineni et al., 2002), CIDEr-D (Vedantam et al., 2015), and ROUGE-L (Lin, 2004).

then the mixing ratio is defined as αv (αv+α f +αe) : Video Captioning Datasets We report results on three popular video captioning datasets.


We observe 4 Statistical significance of p < 0.01 for CIDEr-D and ROUGE-L, p < 0.02 for BLEU-4, p < 0.03 for METEOR, based on the bootstrap test (Noreen, 1989;Efron and Tibshirani, 1994) with 100K samples.

directly generating the entailed hypothesis sentences given a premise sentence would be even more beneficial than retrieving or reranking sentence pairs, because most downstream generation tasks only come with the source sentence and not pairs.


NLP tools with high precision are needed to compute these metrics, which is a problem for Portuguese since no robust dependency or constituency parsers exist.

another problem with transcriptions in our work is their size.


our method is based on domain adaptation, treating the flawed training dataset as a source domain and the evaluation dataset as a target domain.


(2017) use mean squared errors for reconstruction, the former of image pixels and the latter of words in a context window.

we test our approach on a suite of 5 Microsoft Cortana domains with 2 separate tasks in spoken language understanding: (1) intent classification and (2) slot (label) tagging.


For example, in the ATIS dataset, which is collected from an airline travel information service system, the information type includes flight, city, and so on (Tur et al., 2010).

the construction process described above yielded a dataset made up of 4, 833 CHAt and 10, 327 NONCHAt utterances.


In the exploratory phase, the minus term in the equation 9 helps to reduce the score of the sentences whose concepts have received feedback already.

in another strand of research, Díaz and Gervás (2007) create user models based on social tagging and Hu et al.


Table 7: Example poems generated by the C 1 +Style Mem system where the style is romantic.

2, where the energy function of the style memory shown in plot (d) is biased towards a particular style, and once it is added to energy function of the one-iteration model, the resulting energy function shown in plot (g) obtains lower values at locations corresponding to the locations of the memory, which encourages generation of poems with similar styles as those poems in the memory.


For instance, when the market opens, comments usually mention how much the stock price has increased or decreased compared with the closing price of the previous trading day, as in (1) and (3) in Figure 1.

to overcome this problem, Gulcehre et al.


For CNN, we used a single convolutional layer and set the filter size to 3.

interest has recently been growing in automatically learning a correspondence relationship from data to text and generating a description of this relationship since large-scale data in diversified formats have become easy to acquire.


We first split the Google News dataset into a training set, a validation set and a test set.

we propose two major changes to the model by Filippova et al.


Due to the lack of Chinese version of WordNet, several Chinese semantic dictionaries have been conducted, such as Sinica BOW (Huang et al., 2004), SEW (Xu et al., 2008), COW (Wang and Bond, 2013), etc.

the original optimization problem can be decomposed and solved separately according to different candidate hypernyms.


This observation motivates us to approach the reading comprehension problem in a constituentcentric manner, where the generation of candidate answers and their representation learning are both based upon constituents.

on the left is the constituent parse tree of the sentence "it came from the medical faculty in Paris".


The second one is the Linguistic Style Coordination (LSC) metric proposed in (Danescu-Niculescu-Mizil et al., 2011), which quantifies the degree to which one individual immediately echoes the linguistic style of the person they are responding to.

as future work, we are considering to automatize this process by conducting automatic speaker diarization and transcription via automatic speech recognition.


We conducted a new investigation on automatically inferring prerequisite relations among concepts in MOOCs.

random Forest beats others, with best F 1 across all three datasets.


Prerequisite relations essentially can be considered as the dependency among knowledge concepts.

we then formally define the average video coverage (avc) and the average survival time (ast) of a concept a as follows.


Since course concepts are usually introduced based on their learning dependencies, the structure of MOOC courses also significantly contribute to prerequisite relation inference in MOOCs.

where f (a, V) indicates the term frequency of concept a in video V, which reflects how important is concept a to this video.


We study the extent to which the prerequisite relation between knowledge concepts in Massive Open Online Courses (MOOCs) can be inferred automatically.

context information in course videos provides important clues to infer prerequisite relations.


(2012) perform stylistic segmentation of a well-known poem, The Waste Land by T.S.

priors are the ones from the corresponding devsets in Table 1.


Eisenstein and Barzilay (2008) discuss this within the context of topic segmentation.

their method is based on lexical cohesion -expressed in this context as topic segments having compact and consistent lexical distributions -and implements this within a probabilistic framework by modelling words within each segment as draws from a multinomial language model associated with that segment.


The state-of-the-art named entity recognition (NER) systems are supervised machine learning models that require large amounts of manually annotated data to achieve high accuracy.

under this approach, we train a single English NER system that uses only word embeddings as input representations.


For example, the edit tree for the word-lemma pair 'sang-sing' depicted in Figure 1, cannot be applied on the word 'achieving'.

the proposed method considers the task as to identify the correct edit tree representing the transformation between a word-lemma pair.


This enables word types that are highly predictive in context to compete with the probability of a copy event.


Finally, our model disfavors copying numbers from the cache, even when they are available.

they must be generated by the language model component (rather than from the cache).


The MWC contains English (EN), French (FR), Spanish (ES), German (DE), Russian (RU), Czech (CS), and Finnish (FI).

existing character-sequence models have no explicit mechanism for modeling the fact that once a rare word is used, it is likely to be used again.


Furthermore, they address the mismatch between word-level loss and sequence-level evaluation metric by using a mixture of the REINFORCE (Williams, 1992) algorithm and the standard maximum likelihood training to directly optimize a sequence-level loss.

in the following, we will present objectives from Sokolov et al.


A further effect of control variates is to reduce the magnitude of the gradient, the more so the more the stochastic gradient and the control variate covary.

since it is a non-differentiable post-processing step, the NMT model cannot directly be trained for this behavior.


it is easy to use standard stochastic gradient descent algorithms to train our model.


Their model is identical to our S-Distortion model that captures the relative jump distance knowledge on source words.

in recent years, end-to-end NMT (Kalchbrenner and Blunsom, 2013;Bahdanau et al., 2015) has made tremendous progress (Jean et al., 2015;Luong et al., 2015b;Shen et al., 2016;Sennrich et al., 2016;Tu et al., 2016;Johnson et al., 2016).


For example, adding the phrasal verb "ask someone out" as a constraint would mean using "ask" as constraint 0 and "out" as constraint 1 , with two filters: one requiring that constraint 1 cannot be used before constraint 0 , and another requiring that there must be at least one generated token between the constraints.

experiments show that GBS can provide large improvements in translation quality in interactive scenarios, and that, even without any user input, GBS can be used to achieve significant gains in performance in domain adaptation scenarios.


There are multiple factors that can influence the users' rewriting behaviors such as the user's native language, education level and previous revision behaviors, etc.

draft2 to draft3 Totals #Add #delete #Modify #Add #delete # Modify  Content  294  179  33  320  27  16  869  Claims/Ideas  25  8  4  5  0  0  42  Warrant/Reasoning/Backing  166  83  7  191  13  3  463  Rebuttal/Reservation  23  1  0  13  0  0  37  General Content  50  80  18  86  13  13  260  Evidence  30  7  4  25  1  0   when comparing the number of revisions made by Group A and Group B on Rev23 (controlling for their Rev12 numbers), we did not find a significant difference.


Using MCL instead of CW for sense induction in WATSET expectedly produces more finegrained senses.

while we use our approach to synset induction in this work, the core of our method is the "localglobal" fuzzy graph clustering algorithm, which can be applied to arbitrary graphs (see Figure 1).


For the output layer, multi-class classification is performed using the softmax function: denotes a vector representation propagated from the last RNN layer (Fig 3).

for zero arguments (Zero), our models achieved a considerable improvement compared to the joint model in Ouchi et al.


• We collect over 650K question-answerevidence triples, with questions originating from trivia enthusiasts independent of the evidence documents.

the accuracy of the system steadily decreased as the length of the questions increased -with 50% for questions with 5 or fewer words to 32% for 20 or more words.


Following recent formulations (Rajpurkar et al., 2016), we further assume that a i appears as a substring for some document in the set D i .

the oracle score is the upper bound on the exact match accuracy.


3 Section 4 shows that this assumption is valid over 75% of the time, making evidence documents a strong source of distant supervision for training machine reading systems.

for web search results, we expect the documents that contain the correct answer a to be highly redundant, and therefore let each questionanswer-document tuple be an independent data point.


ten achieve near-human performance levels within months or a year, fueling a continual need to build ever more difficult datasets.

we consider two types of distant supervision, depending on the source of our documents.


For web search results, we expect the documents that contain the correct answer a to be highly redundant, and therefore let each questionanswer-document tuple be an independent data point.

2 we differ by setting D i as a set of documents, where previous work assumed a single document (Hermann et al., 2015) or even just a short paragraph (Rajpurkar et al., 2016).


(WSD) task, we use the context information to distinguish existing mention senses, or create a new out-of-KB sense.

we solve it by finding the closest word vector w ?


Besides, by introducing entity embeddings into our MPME framework, the knowledge information will also be distilled into mention sense embeddings, so that the mention sense Memorial Day will be similar as Independence Day (US).

we extend Skip-gram model to a network by maximizing the log probability of being a neighbor entity.


The execution phase starts with a human request for action (e.g., boil the water).

recent years have seen an increasing amount of work on task learning from human partners (Saunders et al., 2006;Chernova and Veloso, 2008;Cantrell et al., 2012;Mohan et al., 2013;Asada et al., 2009;Mohseni-Kabir et al., 2015;Nejati et al., 2006;.


These questions are designed to address ambiguities in noun phrase grounding, uncertain environment sensing, and goal states.

in our view, interactive learning beyond demonstration of primitive actions should play a vital role in the robot's acquisition of more reliable models of grounded verb semantics.


Figure 5 shows the performance of the various learned models on the testing data, based on a varying number of training instances and different interaction policies.

for verb semantics, recent works explored the connection between verbs and action planning Misra et al., 2014Misra et al., , 2015She and Chai, 2016), for example, by representing grounded verbs semantics as the desired goal state of the physical world that is a result of the corresponding actions.


To avoid type I errors we corrected for multiple comparisons using the Holm-Bonferroni correction.

we avoid statistical tests combining two or more factors since some of the resulting groups would be empty due to the skewed group sizes along race.


In order to study representation of the different subgroups as major characters in movies, we construct a network of interaction between characters using which we compute importance measures for each character.

female writers and directors appear to produce movies with relatively balanced gender proportions (still slightly skewed towards the male side) compared to male writers Studies report potential biases in actor employment with age (Lincoln and Allen, 2004), particularly in female actors.


The forward and backward LSTMs are respectively formulated as follows: where g (LST M ) is the same as that in Eq (1).

t+1 is the sentiment distuibution after transformation, T x j ∈ θ is the transformation matrix for a negation word x j , a parameter to be learned during training.


We present linguistically regularized LSTMs for sentence-level sentiment classification.


This is because the useful sentiment information in domain-specific sentiment similarities mined from unlabeled samples and the actively selected labeled samples of target domain is not fully exploited.

sentiment classification is widely known as a domain-dependent problem (Liu, 2012;Pang and Lee, 2008;Blitzer et al., 2007;.


The detailed statistics of this dataset are summarized in Table 1.

many researchers have found that unlabeled instances with high uncertainties can be outliers, whose labels are useless and even misleading (Settles, 2010;Zhu et al., 2010).


Given the final feature vector x with l dimensions, we apply SVM as a well-known method for training both regression and classification methods.

we specifically explore the use of recent Information Retrieval (IR) term weighting models that are effectively extended by related terms using word embeddings.


to verify the effect of the size of training data, we train a sector-agnostic model for each sector.


we need to consider the size of the training set.


Investigating in the reports, we observe the broad use of the expression 'beneficial owner' which is normally followed by riskfull sentences since the beneficial owners can potentially influence shareholders' decision power.

the networks could not provide superior results than the SVM regressors.


Besides, we set the number of negative samples k to 1 in CANE to speed up the training process.

we implement CANE on text-based information networks with proposed mutual attention mechanism, and conduct experiments on several real-world information networks.


We then develop, in Section 4, the first latent contextfree representation for the set of noncrossing digraphs, then extended in Section 5 with additional latent states supporting our finite-state axiomatization of digraph properties, and allowing us to control the search space using the lexicon.

under no constraints, we simply set A = A.


The nonderivative representations for our axiomatic properties share the same Dyck language D 55 and homomorphism, but differ in terms of forbidden patterns.

1 A polytree (Rebane and Pearl, 1987) is a multitree whose underlying graph is a tree.


Digraph Ontology The context-free properties of encoded digraphs have elegant nonderivative language representations and they generate a semi-lattice under language intersection.

if we know that the parses have some restrictions, it is better to use them to restrict the search space as much as possible.


", the agent should focus on her friends who went to Columbia University.

formally, we carry over the last utterance embedding h t,0 = h t 1,n t 1 and define: where c t,j is a weighted sum of node embeddings in the current turn: c t,j = , where ↵ t,j,v are the attention weights over the nodes.


We believe this happens because distributional models keep related, yet antonymous words close together (e.g.

requests: similarly, the proportion of dialogue turns where user's requests for information were identified correctly.


We randomly initialize embedding vector for each entity type (including the NA type) and update it in training procedure.

aNN-S2 achieves higher recall with a little loss of precision.


Proof Any segmentation of the sentence of length M starts with either a segment of length 1, a segment of length 2, • • • , or a segment of length L. Thus, g(M ; L) can be defined through the following recurrence relation: together with the initial values g(1; L), g(2; L), • • • , g(L; L), which can be computed offline (for example, for L = 3, one has: g(1; 3) = 1, g(2; 3) = 2, g(3; 3) = 4).

the main difference with the previous knowledgefree topic model approaches is that the proposed approach assigns topics to words based on two, segment-specific and document-specific distributions selected from a Bernoulli law.


Instead, different network structures are constructed dynamically as our learning procedure explores the state space.

as discussed above, we use approximate reward functionR θ instead of the true reward.


In a fully supervised setting where the correct semantic parse of each question is available, learning the policy function can be reduced to a sequence prediction problem.

in addition, we also disallow adding duplicates of actions that already exist in the state.


Documents consist of 20 contiguous sentences from the body of a popular children's book, and queries are formed by deleting a token from the 21 st sentence.

gA statistically significantly outperforms all other baselines on 3 out of those 4 datasets at the 5% significance level.


We are also interested in how word embedding can help group domain-specific texts into clusters.

at the core of solving the above formulation is an optimization method that searches the Wasserstein barycenters of varying partitions.


Current neural models have managed to successfully represent complex semantic associations by effectively analyzing large amounts of data.

in the network of Kim (2014), the pooled features are directly passed to a fully connected softmax layer whose outputs are class probabilities.


Moreover, unlike supervised WSD and entity linking techniques (Zhong and Ng, 2010;Cheng and Roth, 2013;Melamud et al., 2016;Limsopatham and Collier, 2016), our algorithm relies only on semantic networks and does not require any senseannotated data, which is limited to English and almost non-existent for other languages.

thanks to the fact that words that refer to the same meaning in context, e.g., Formula 1-F1 or German Grand Prix-German GP-Hockenheim, are all disambiguated to the same concept, the co-reference issue is also partly addressed by our pipeline.


They were written with a specific paragraph in mind, thus their language can be ambiguous when the context is removed.

open-domain QA was originally defined as finding answers in collections of unstructured documents, following the setting of the annual TREC competitions 1 .


SQuAD questions have been collected via a process involving showing a paragraph to each human annotator and asking them to write a question.

a second motivation to cast a fresh look at this problem is that of machine comprehension of text, i.e., answering questions after reading a short text or story.


We compute q = j b j q j where b j encodes the importance of each question word: , and w is a weight vector to learn.

kBs have inherent limitations (incompleteness, fixed schemas) that motivated researchers to return to the original setting of answering from raw text.


A bigram is simply the conjunction of a word w and previous word, which we will denote as w -1 ; i.e., bigram = w -1 &w. Similarity, a trigram is the conjunction of w -2 and bigram.

local domains and local functions are analogous to factors and factor potentials in a factor graph.


That is, for any x, y, z ∈ K, we The additive identity is an annihilating element with respect to multiplication.

each local domain is associated with a function that maps variable assignments to feature extractors.


For example, the weight associated with the edge connecting nodes E and D in the fig- Figure 1: The junction tree for our running example.

from the computational point of view, the process of feature extraction is typically treated, at best as the preprocessing step of caching featurized inputs over entire datasets, and at worst, as 'somebody else's problem'.


In contrast, in our word-level decoder, the hidden state iterates only over the length of a chunk and then generates an end-of-chunk token.

the model described in this section is the basis of our proposed decoders.


While the former gate allows the model to forget the previous states, the latter gate decides how much the model updates its content.

our word-level decoder is released from the pressure of memorizing the long (interchunk) dependencies and can focus on learning the short (intra-chunk) dependencies.


(2016) when translating into German, with improvements of +1.4 BLEU and +2.7 METEOR.

we first briefly revisit the attentionbased NMT framework ( §2) and expand it into an MNMT framework ( §3).


We use publicly available pre-trained CNNs for image feature extraction.

we observe that in general visually grounded terms, e.g.


Our MNMT model can be seen as an expansion of the attention-based NMT framework described in §2.1 with the addition of a visual component to incorporate spatial visual features.

we extract spatial image features for all images in our dataset using the 50-layer Residual network (ResNet-50) of .


Therefore, both methods miss this information in the translated French sentence.

table 4 shows BLEU scores on the Europarl corpus of our proposed methods.


We run beam search on the pivot sentence with the teacher model and choose the highest-scoring target sentence as the mode.

as the parameters of the teacher model are fixed, the training objective can be equivalently written as: where our goal is to find a set of source-totarget model parameters that minimizes the training objective: We use similar approaches as described in Section 3.2 for approximating the full search space with sentence-level teaching.


Surprisingly, our method outperforms all other methods.

a major difficulty faced by our approach is the intractability in calculating the gradients because of the exponential search space of target sentences.


Currently, we are only using the structure information from the tree without the syntactic labels.

although Tree-Lstm with LSTM based sequential model obtain a slightly better result(+0.22 BLEU, v.s.


In all, we demonstrate an improvement of +3.54 BLEU over a standard attentional NMT system, and +1.90 BLEU over a stronger NMT system with a Tree-LSTM encoder (Eriguchi et al., 2016) and a coverage model (Tu et al., 2016).

we build our model on top of the word coverage model proposed by Tu et al.


We used the Google N-Gram corpus (Michel et al., 2010).

when assigning a score to a word pair, the current state of the art methods do not take into account scores assigned to other word pairs.


Now, we use the same dev, test and highresource train sets as in §5.1.

furthermore, for most languages in the world, highcost linguistic annotation and resource creation are unlikely to be undertaken in the near future.


The target language is indicated by "→", e.g., it is Spanish for "→ES".

alternative methods have been proposed, e.g., to port a model trained on the source language to the target language (Shi et al., 2010).


(2016) showed that the distance between the attentioninfused alignments and the ones learned by an independent alignment model can be added to the networks' training objective, resulting in an improved translation and alignment quality.

(2008), dividing the data for each inflection type into five folds, each consisting of 500 training, 1000 development and 1000 test examples.


In light of character-level results, they imply that current unsupervised morphological analyzers are poor substitutes for real morphological analysis.

a morph is the surface realization of a morpheme (Morley, 2000), which may vary from word to word.


the computation of the j-th element of f is defined as where A, B = Tr(AB T ) is the Frobenius inner product and b is a bias.


Note that If we choose W = U Σ, we exactly obtain w 1 , w 2 = s(w 1 , w 2 ), since W W = XX in this case.

as an example, how to find whether the word "table" is semantically more similar to the word "stool" than to the word "sky"?


Even syntactic formalisms are moving toward graphs (de Marneffe et al., 2014).

it uses a single shared BiLSTM encoder, and keeps the inference separate for each task.


(3) The SAT model performs much better than SSA and SAC.

each word in SSA model still has only one single representation in different contexts, which cannot deal with polysemy of most words.


We also hope to apply the model to other languages with complicated compositional writing systems, potentially including historical texts such as hieroglyphics or cuneiform.

this one-hot vector is then multiplied by the lookup matrix t C ∈ R |C|×dc , where d c is the dimension of the character embedding.


On the one hand, the model reuse previously learned features from left columns via lateral connections (i.e., adapters).

progressive networks also have ability to transfer knowledge from previous tasks to improve convergence Figure 3: Each column is a stacked bidirectional LSTM RNN model.


However, it is well known that adding data in very different genre to training data may hurt parser performance (Bikel, 2004).

and data sparsity has been a bottleneck which can not be 1 http://www.klcl.pku.edu.cn/ShowNews.aspx?id=156 ] 。 Figure 1: Sentences from (a) CPB and (b) our heterogeneous dataset.


For simplicity we use the mini-batch for this set, but it could be a different set.

the second source of data is a set of sentence pairs automatically extracted from Simple English Wikipedia and English Wikipedia articles by Coster and Kauchak (2011) systems, where each instance pairs a simple and complex sentence representing approximately the same information.


We refer to this type of model selection as test.

the reason for the performance gap, and how to correct it, was left as an open problem.


(2014), 2) the predictions of our baseline model LSTM-PP, 3) the predictions of our improved model OntoLSTM-PP, and 4) the gold labels Oracle PP.

(2014), referred to as HPCD (full) in the paper.


That is, context f (i, t) = lstm in(t i−1 ) for the forward RNN and context b (i, t) = lstm in(t i+1 ) for the backward RNN.

we note there is a relatively small difference in unlabeled attachment accuracy for all dependencies (not only PP attachments), even when gold PP attachments are used as additional features to the parser.


The number of vectors learned is still fixed, based on the preset number of senses.

(2014), we used RBG parser , and modified it by adding a binary feature indicating the PP attachment predictions from our model.


These approaches all share the limitation that, in practice, in order for a class to be populated with instances, the entire class label has to have been observed verbatim in text.

this allows us to extract instances even when the full class label never appears in text.


Second, when class labels are treated as though they cannot be decomposed, every class label must be modeled independently, even those containing overlapping words ("American jazz musician", "French jazz musician").

the "universal schema" approach (Riedel et al., 2013;Kirschnick et al., 2016;Verga et al., 2017), which infers relations using matrix factorization, often includes Hearst patterns as input features.


If sr k < sl k+2 , e (sl k ,sr k ) and e (sl k+2 ,sr k+2 ) share no common endpoint, violating 1EC.

for sub-problems L, R, LR, and N, each has O(n 3 ) elements, with a unit calculating time O(n).


This modification can be applied to several common sequence labeling architectures and requires no additional annotated or unannotated data.

we propose an additional training objective which allows the models to make more extensive use of the available data.


We also present an analysis of the failures.


The all rows in Table 4 show the number of pairwise links that are created by deep-coref on the development set for different mention types.

to ensure more meaningful improvements, we ask to incorporate out-of-domain evaluations in the current coreference evaluation scheme.


We further analyze the output of deep-coref on the development set.

this genre includes a large number of pronouns.


The LSA summarizer ranks among the best systems in TAC2009 with pearson's r but is closer to the worst systems in TAC2008.

this supports future work on identifying an "optimal" θ as a model of relevant quality aspects of a summary.


Table 1 shows the mean value for each group and the t-statistic for each of the features.

in either case, the participant is tapping into semantic subcategories when producing his response.


Unlike the mainstream approach that trains one monolingual parser per source language, our approach integrates N encoders, one for each language, into a single model.

the evaluation metric is defined in terms of exact match accuracy with the ground-truth logical forms.


rejecting 10% of candidates; and Area under a model's rejection curve (AUC) (Fig 3).

the input features where whitened by subtracting the mean and dividing by the standard deviation for each dimension computed on all training speakers.


@username R u a wizard or wat gan sef: in d morninu tweet, afternoon -u tweet, nyt gan u dey tweet.

for example, a misclassified Bosnian tweet Sočnǐ cokoladni biskvit recept ("juicy chocolate biscuit recipe") would be the same in Croatian.


RDK ({}) * last tweet about you --, maybe Figure 1: Challenges for socially-equitable LID in Twitter include dialectal text, shown from Nigeria (#1) and Ireland (#2), and multilingual text (Indonesian and English) in #3. graphic and dialectal variation.

a Hindi-labeled tweet dost tha or rahega ... dont wory ... but dherya rakhe ("he was and will remain a friend ... don't worry ... but have faith") is a characteristic example, misclassified by our system as English.


In this work, we introduce a sociallyequitable LID system, EQUILID, built by (1) creating a dataset representative of the types of diversity within languages and (2) explicitly modeling multilingual and codes-switched communication for arbitrary language pairs.

we include corpora from different levels of formality across a wide range of topics.


We aim to find a suitable alternative extrinsic evaluation for compound splitting.

as closed compounding introduces semantic opaqueness and vastly increases the vocabulary size of a language, many NLP tasks benefit from compound splitters.


Our proposed extrinsic evaluation approach for compound splitting is language-independent as we do not use any language-specific parameters.

additionally, to provide an upper bound, we manually split development and test data.


10 out of its 29 (34.5%) misclassifications compared to FF2010 can be attributed to oversplitting, while 4 (13.8%) missclassifications are due to undersplitting.

since most compounds (in particular ad-hoc productions) are compositional, this is only a side issue.


500 features were sampled from the top 20,000 unigrams 4 , 2000 social media LDA topics (Schwartz et al., 2013a), and all 64 categories from the LIWC lexica (Pennebaker et al., 2007).

note that at higher levels of analysis (at user and county levels) as the distribution of features becomes closer to Normal, modeling features as Bernoulli is clearly sub-optimal where as at the message level modeling unigrams as a Bernoulli is superior.


We presented a first attempt at modelling sentence simplification with a neural sequence to sequence model.

we generate the first two candidate hypotheses for each beam size from 5 to 12.


It has attracted much attention recently as it could make texts more accessible to wider audiences (Aluísio and Gasperin, 2010;, and used as a pre-processing step, improve performances of various NLP tasks and systems (Vickrey and Koller, 2008;Evans, 2011;Stajner and Popović, 2016).

10 Second, three native English speakers rate the grammaticality (G) and meaning preservation (M) of each sentence with at least one change on a 1-5 Likert scale (1very bad; 5 -very good).


The most common approach to crowdsourcing paraphrase generation is to provide a sentence as a prompt and request a single paraphrase from a worker.

correctness decreases along the chain, suggesting the need to check paraphrases against the original sentence during the overall process, possibly using other workers as described in § 2.1.


also during training time, we randomly replace 10% of the input words by an artificial UNK token, which is then used to replace all unseen words in the development and test sets.


At test time, these models require a relatively expensive recognition algo-rithm (Collins, 1999;Titov and Henderson, 2007) to recover the parse tree, but the parsing performance consistently lags behind their discriminative competitors (Nivre et al., 2007;Huang, 2008;Goldberg and Elhadad, 2010), which are directly trained to maximize the conditional probability of the parse tree given the sentence, where linear-time decoding algorithms exist (e.g., for transition-based parsers).

we use the following features: 1) the stack embedding d t which encodes the stack of the decoder and is obtained with a stack-LSTM (Dyer et al., 2015(Dyer et al., , 2016; 2) the output buffer embedding o t ; we use a standard LSTM to compose the output buffer and o t is represented as the most recent state of the LSTM; and 3) the parent non-terminal embedding n t which is accessible in the generative model because the RNNG employs a depth-first generation order.


With n-best rescoring, all neural network-based systems achieve significant improvements over the phrase-based system.

in the conventional approach, the Bayes' theorem is used and the HMM is applied to the inverse translation model Pr(e i 1 |f J 1 ) = Pr(e i 1 ) in this case, as a part of a noisy channel model, the marginalisation becomes intractable for every e. This work proposes a novel concept focusing on direct HMM for Pr(e i 1 |f J 1 ), in which the alignment direction is from target to source positions.


We also have a cascade classifier with a tertiary classifier, which is invoked in the same fashion as the secondary classifier after the primary and secondary classifiers have been run.


While the success and unmet desire cases could be understood as having or lacking something, the planning cases indicate steps achieved toward a desired end-state.

the triples they learn do not focus on first-person experiencers.


The first 4 datasets are from (Hu and Liu, 2004), which actually has 5 datasets from 4 domains.

m can leverage some reliable prior knowledge in A 1 , .


When faced with a new domain D n+1 , K allows the general dependency feature to generate more dependency patterns related to aspects due to more knowledge labels 'A' as we explained in the previous section.

if the context word in the dependency relation appears in K t , we replace it with a knowledge label 'A' (aspect); otherwise 'O' (other).


For the biomedical datasets, we use word2vec trained on biomedical texts (Moen and Ananiadou, 2013) 11 to initialize E p .

for the biomedical datasets, we use the Medical Subject Headings (MeSH) terms 8 attached to each abstract to classify them.


Recent work on neural constituency parsing (Dyer et al., 2016;Choe and Charniak, 2016) has found multiple cases where generative scoring models for which inference is complex outperform base models for which inference is simpler.

mS is supported by an NSF Graduate Research Fellowship.


(2016), and the LSTM language modeling generative parser (LM) of Choe and Charniak (2016).

while this search procedure allows us to explore these generative models, disentangling reranking and model combination effects, the increase in performance from augmenting the candidate lists with the results of the search may not be worth the required computational cost in a practical parser.


Let A be a parser that we want to parse with (here one of the generative models), and let B be a base parser that we use to propose candidate parses which are then scored by the less-tractable parser A.

we do obtain a gain over state-of-theart results using simple model score combination on only the base candidates, which can be implemented with minimal cost over the basic reranking setup.


W as follows: (3) The divergence measure JSMI α (X, Y ) can be alternatively defined in terms of mutual information between W and Z.

we used the Penn Tree Bank (PTB), a popular small-scale corpus, for our experiments.


We showed that the optimization of skip-gram embeddings with negative sampling finds the best low-dimensional approximation of the JSMI measure.

there are several standard methods of measuring the distance between two discrete probability distributions, defined on a given finite set A.


We can represent a given d-dimensional embedding by a matrix m, such that m(x, y) = x • y.

in practice, we can approximate the expectation by sampling of 'negative' examples.


This work was partially supported by National Natural Science Foundation of China (61572049 and 61333018).

we identify span and nuclearity simultaneously in the transition-based tree construction, since nuclearity is actually closely related to the tree structure, just as the left-arc and rightarc action in dependency parsing.


These new actions do not add an arc between tokens but instead they edit a single token.

the edits are represented by [-deletion-] and {+insertion+}.


As a baseline, we use the original parser as described by GE, which is equivalent to EREF with training on an error-free corpus (E00).

the parser is able to jointly parse and correct grammatical errors in the input sentence.


Both datasets also use synthetic images and emphasize representing diverse spatial language.

procedures for submitting models and the task leader board are available at http://lic.nlp.cornell.edu/nlvr.


Possible values for these attributes are presented in Table 2.

adding cTAKES features degrades it slightly (-0.013).


• Hierarchical structures outperform the nonhierarchical one.

our explicitly weighting approach could be viewed as heuristic attention.


The performance is shown in row 9 in Table 1.

according to this theory, different types of arguments invite specific types of critiques.


We investigate the extent to which it is possible to close the performance gap between topicspecific and across-topics models for identification of good arguments.

argumentative essays often have a fairly standard structure, where a general claim (or stance, or thesis) on the issue is introduced in the beginning of the essay, followed by a sequence of main points, each elaborated using supporting statements, and finally followed by a conclusion that often re-states the thesis and provides a high-level summary of the argument.


Still, we conclude that the crowd generally handles the theory-based quality assessment almost as well as the experts.

we now report on experiments that we performed to examine to what extent the theory and practice of argumentation quality assessment match.


Tables 5(c) and 5(d) reveal the reason behind, namely, the results of crowd group 1 and group 2 differ clearly.

we asked the crowd to rate the same 304 arguments as the experts for all 15 given quality dimensions with scores from 1 to 3 (or choose "cannot judge").


It is interesting to note that RSTbased discourse features generally have higher correlations with the holistic speaking proficiency scores than with the more specific discourse coherence scores.

human annotators were trained in a two-step process: 1) after a comprehensive study of the annotation guidelines described in Section 3.1, the two annotators were initially trained with 16 TPO responses (8 responses from an Independent question and 8 responses from an Integrated question) by first performing independent annotation and then resolving all disagreements through a discussion of the guidelines; 2) another round of training was conducted on an additional set of 32 TPO responses (8 responses from an Independent question and 24 responses from an Integrated question).


The average number of EDUs per response in the two batches in this set of 120 responses were 15.1 and 14.1.

spoken responses are relatively shorter and comprise simpler discourse structures with fewer relations, which simplifies the RST annotation task in comparison to written text.


where ⊕ indicates concatenation, s t and m t are respectively a hidden state and a memory cell at time t, and e 1 (word ) is embedding of word.

we consider it is because various types of deletion make the compression model more flexible, as indicated by closer CR of the model trained on multi-root+ instead of rooted to the average CR of the test data.


Chinese, Japanese and Korean stand out, with improvements as high as 19.17 points f-score due mostly to large increases in recall.

different words representing the same event often contain similar characters.


Building knowledge bases to store, search, and reason over this information is an important problem in natural language understanding.

rELATED was posed as a binary task of whether m r is more related to the query or the intruder (without highlighting m q ).


Consider the following question from an Alaska state 4th grade science test: Which object in our solar system reflects light and is a satellite that orbits around one planet?

bad Alignment: Which of the following gases is necessary for humans to breathe in order to live?


Comparing our method with two state-of-the-art systems for 4th and 8th grade science exams, we demonstrate that (a) TUPLEINF with only automatically extracted tuples significantly outperforms TABLEILP with its original curated knowl-   (Howell, 2012) at p = 0.05.

a table-knowledge centered inference model isn't the best fit for noisy tuples.


Other approaches use tree kernels (Xu et al., 2013), qualia-based patterns (Xavier et al., 2015), and simple within-sentence inference (Bast and Haussmann, 2014).

in case of a successful match, a seed fact of the form (arg headword; relation headword; quantity; unit) is generated.


One of the first Open IE systems to obtain substantial recall is OLLIE (Mausam et al., 2012), which is a pattern learning approach based on a bootstrapped training data using high precision verb-based extractions.

none of them handle numbers specifically, and hence do not work for our problem.


This results in a set of phrases that act as textual evidence for relations of an entity pair.

our most expressive model encodes a fact as X = (e s , r, e o , t s , t o , T o,s ), where t s , t o are associated representation of types of the two entities, and T o,s is the aligned textual evidence associated with a pair of entities from a side corpus.


An extension to the Universal Schema approach was proposed by (Toutanova et al., 2015), where representations of text relations are formed compositionally by Convolutional Neural Networks (CNNs) and then composed with entity vectors by a bilinear model to score a fact.

best models were chosen among 20 epochs of training by monitoring validation MRR.


At the cost of restricting the permissible type sets slightly, we can speed up the greedy procedure further.

prior work on finegrained entity typing has derived its type system from Freebase (Ling and Weld, 2012;Gillick et al., 2014).


A convolution operates a filter w ∈ R n×e to a window of n words x i,i+n with bias term b by a i = σ(w • x i,i+n + b ) with non-linear activation  W,b (•) Figure 4: Group Sparse CNN.

different ways of initializing the projection matrix in Eq.


Then we get g centroids from each category by kmeans.

traditional techniques treat questions as general sentences, ignoring the corresponding answer data.


Keyphrase detection methods specific to the scientific domain often use keyphrase gazetteers as features or exploit citation graphs (Hasan and Ng, 2014).

here, we follow the probably most common approach to multi-task learning, known as hard parameter sharing.


We perform experiments for both keyphrase boundary identification (unlabelled), and keyphrase boundary identification and classification (labelled).

previous methods relied on corpora annotated for type-level identification, not for mention-level identification (Kim et al., 2010;Sterckx et al., 2016).


Open IE methods (Mausam et al., 2012;Del Corro and Gemulla, 2013) capture triples (or quadruples) such as Obama, has, two children .

stateof-the-art Open-IE systems either hardly extract cardinality information or fail to extract cardinal-   Definition We define a mention that expresses relation cardinalities as the following: "A cardinal number that states the number of objects that stand in a specific relation with a certain subject."


We presented an intuitive method for mapping FactBank and MEANTIME onto the UW scale, and presented a novel factuality model which extends TruthTeller and applies it over PropS' abstraction of dependency trees.

the rule-based performance is well correlated with the gold predictions on FactBank and UW, showing its off-the-shelf usability.


This work was supported in part by the Center for Intelligent Information Retrieval and in part by DARPA under agreement number FA8750-13-2-0020.

using every pair of entities, we create a memory cell similar to as in universal schema.


While scheduled sampling has been empirically successful, its training objective has a drawback: because the procedure directly incorporates greedy decisions at each time step, the objective is discontinuous at parameter settings where previous decisions change their value.

in our approach, described in detail in the next section, we attempt to fix this problem by incorporating a continuous relaxation of the argmax operation into the scheduled sampling procedure in order to form an approximate but fully continuous objective.


The empirical results, discussed later, show similar gains to the greedy scenario.

our end-to-end relaxed greedy training objective is differentiable everywhere and fully continuous.


Additionally, it is interesting to extend this approach to other tasks like sentence compression and text abstraction.

unlike previous works, we adapt multi-source NMT for system combination and design a good strategy to simulate the real training data for our neural system combination.


For the Chinese-to-Japanese tasks, we trained a joint BPE model on both of the Chinese and Japanese vocabularies, because Chinese and Japanese could share some vocabularies of Chinese characters.

the NtCIR-CE task uses 1M, 2k, and 2k sentences for training, development, and testing, respectively.


We believe the performance difference between the two tasks is due to their unique characteristics.

it is reported that NMT works better than SMT only when there is an abundance of parallel corpora.


We note that at the same hash size, features with a larger space perform worse.

in NLP, bag-of-words models require a full pass over the data to identify the vocabulary for each feature type (e.g.


The Hit@k rate regards a test instance as correct if the answer is ranked within the top k places.

we regard one test instance as correct if the answer is ranked within the top max(1, len * r% ) candidates.


Finally, the model produces the pixel width of the space after each medial comma, s ik , again conditioned on the compositor, c i .

the weight of a single feature is difficult to interpret in isolation.


(2) A caption must follow the da/dearu style (one of writing styles in Japanese).

in addition, we compare it to YJ Captions (Miyazaki and Shimizu, 2016), a dataset with Japanese captions for the images in MS-COCO like in STAiR Captions.


First, we compare various models using text features only.

statistical approaches to combating fake news has been dramatically limited by the lack of labeled benchmark datasets.